Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia

Mislan,; Haviluddin, .; Hardwinarto, Sigit; Sumaryono, Sumaryono; Aipassa, Marlon Ivanhoe

doi:10.1016/j.procs.2015.07.528

Cited by 124 publications

(49 citation statements)

References 10 publications

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“…Beberapa peneliti menggunakan metode Logika Fuzzy untuk menghadapi berbagai kondisi yang tidak pasti/jelas (Asklany et al, 2011;FallahGhalhary et al, 2009;Hasan et al, 2013) sedangkan beberapa peneliti yang lain menggunakan metode Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat (Awan and Maqbool, 2010;Khidir et al, 2013;Mislan et al, 2015).…”

Section: Tinjauan Pustakaunclassified

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

Utomo

Mahmudy

Anam

2017

JTIIK

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AbstrakPrakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211. Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseries Abstract Rainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is

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Section: Tinjauan Pustakaunclassified

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

Utomo

Mahmudy

Anam

2017

JTIIK

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“…rainfall-runoff models up to a few hours (Hung et al, 2009) or monthly amounts (Wu et al, 2015;Mislan et al, 2015)). For daily predictions, Weerasinghe et al (2010) used a feedforward back-propagation neural network for daily rainfall prediction in Sri Lanka, which was inspired by the chain-dependent approach from statistics.…”

Section: Introductionmentioning

confidence: 99%

An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Cramer

Kampouridis

Freitas

et al. 2017

Expert Systems with Applications

169

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Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work's main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.

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“…Particularly draughts, floods and tropical cyclones. Most of the area located at the ocean coast is highly prone to flash floods which are influenced by the monsoons [1] [2]. The livelihood of the poor in these areas will be most threatened by these problems due to their limited adaptive capacities [2].…”

Section: Introductionmentioning

confidence: 99%

“…The change in climate means a change in their life as well whether it is for good or bad. For example, changes in rainfall occurrences and variability affect economics and infrastructure in countries such as Indonesia and Malaysia [1] [2]. There are a lot of studies with different approaches have been conducted to see how much the climate change vulnerability affect their life [3].…”

Section: Introductionmentioning

confidence: 99%

Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia

2018

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Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (ND-GAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.

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Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia

Cited by 124 publications

References 10 publications

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Change Vulnerability Forecasting Using Deep Learning Algorithm for Southeast Asia

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